Forecasting long-term bridge deterioration conditions using artificial intelligence techniques Online publication date: Tue, 14-Apr-2015
by Patrick A. Creary; Fang Clara Fang
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 13, No. 4, 2014
Abstract: About a quarter of 600,000 bridges in the USA are deficient. The objective of this study is to make accurate predictions of future bridge deterioration condition using artificial neural networks, and to ensure the developed models are applicable for practical use. Using bridge inspection data provided by the Connecticut Department of Transportation (ConnDOT), artificial neural networks-based model is developed to modelling complex relationships between input and output to identify patterns within the data. The developed neural net input variables included bridge geometry, construction and service, while the output variables were condition ratings for the deck, superstructure, and substructure. The neural nets used in this research demonstrated an ability to produce accurate results down to a root mean square error of 10.05% on the best trial. This study shows the potential to develop a tool to predict the future condition ratings of bridges to assist agencies in bridge program planning.
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